The control of an industrial process in a plant involves maintaining the process conditions at setpoints that are suitable to attain the desired process objectives. The control of industrial processes involves a series of steps, generally including: determining the desired process objectives; determining initial values for the process condition setpoints for the controllable process states; measuring the existing process conditions and adjusting control variables in accordance with the desired process condition setpoints; measuring the results in terms of the plant outputs and updating the process condition setpoints and/or the control variables to attain the desired process objectives.
The control variables are regulated by a controller that provides settings for the control variables as plant inputs to operate the plant. In a large plant, there may be a large number of inputs, with complex interactions between the inputs contributing to the output of the plant. The plant is generally non-stationary. One or more process conditions may vary constantly; the process typically will not reach a steady state. For example, a fossil-fuel furnace has properties that change over time; soot may accumulate in the furnace and require periodic cleaning. In order to maintain the desired process condition setpoints and satisfy the process objectives continuously, given the variation in process conditions, it is generally necessary to make adjustments repeatedly to the process condition setpoints and the corresponding control variables. Experimenting with different process conditions and different control variables to achieve the desired output typically requires high overhead, particularly in terms of time. In this dynamic environment, experimenting with different values may not even be possible. Direct measurement of the process conditions and the outcome of the process is often difficult to do effectively.
Using models of the process is one approach to addressing some of these control issues. An indirect controller uses a computer model of the process as a predictor of different values in the system. The model mimics the operation of the system. The model's ability to predict values in the system is useful for determining the result of various adjustments to the process inputs, including the control variables in the system, and conversely, determining the control variable settings necessary to achieve desired process outputs or process conditions. Accordingly, such models are useful for adjusting the inputs to the plant under the prevailing plant operating conditions. Neural networks are one such type of computer model that is useful to predict, control and optimize a process.
Indirect control schemes are typically implemented in two phases, wherein the system model is constructed first, followed by the construction of the controller and its corresponding control algorithms. The resulting controller provides the control laws for the plant. Subsequently, during execution, the controller investigates the system model to obtain optimal settings and then implements them using control algorithms. The system model can be retrained. Indirect controllers may use any number of model architectures and adaptation methods. Given a model architecture, adaptation is used to develop the system model. A system model is typically trained by presenting it with training data values of the actual historical operation of the plant. One step that requires a significant investment of resources is the generation of the data used to train the system model. A system model is useful only if it predicts the operation of the plant with a high degree of accuracy. Ensuring the integrity of the system model is critical to optimizing the operation of the plant. To develop an accurate model, it is important to provide comprehensive test data.
One factor in the usefulness of a control system for a plant is its ability to adapt to the constantly (viewed over the long term) fluctuating relationship between the control variables, and process conditions setpoints and the outputs that characterize the plant. In indirect controllers, the system model itself may be adaptive in that it may be able to relearn and adjust the relationships between the plant variables. Two general classes of modeling methods that can be used in indirect controllers are adaptive in this sense: parametric adaptive and strictly non-parametric (with an adaptive architecture and adaptive parameters). In parametric adaptive modeling methods, the architecture is predetermined and the parameters are adaptive. Examples of parametric adaptive modeling methods include regressions and neural networks. Strictly non-parametric methods have no predefined architecture or sets of parameters or parameter values. One form of strictly non-parametric methods is commonly known as evolutionary (or genetic) programming. Evolutionary programming involves the use of genetic algorithms to adapt both the model architecture and its parameters. Evolutionary programming uses random, but successful, combinations of any set of mathematical or logical operations to describe the control laws of a process or to construct a system model.
An adaptive controller generally requires a mechanism by which the controller identifies the need to adapt. A known method to determine when to perform model adaptation is to initiate adaptation at predetermined scheduled times or at regular intervals. One disadvantage of this method is that it performs model adaptation in a predetermined manner, with no regard for when the model error is acceptable or unacceptable, which is inefficient, resulting in downtime for the plant or unnecessary use of computational resources.
One known method to operate the plant when the system model is not considered usable and even for initial training of the system model is to provide control by human operators. One disadvantage of this method is that human control is inadequate to truly optimize process control as a function of the large number of controllable variables that characterize complex plant operations and is subject to constraints such as variability from human to human, variability from day to day, and variability within the duration of an operator's shift.
There is a need for an improved method for providing for adaptation of an indirect controller. There is also a need for an improved method for modeling a system in an indirect controller. There is also a need for providing for coordination between a direct controller and an indirect controller.